From Fig. REF  it can be noted that the same two patches are producing the smallest RMSE in image-to-image translation with the Vanilla GAN and LSGAN. From the same figure, it can be noted that all three objective functions seem to be approximately equally appropriate for translating from \({\mathcal {X}}\)  to \({\widetilde{z}_{x}}\in {\mathcal {Z}}\)  when patches from the two domains have similar appearance, but struggle when the \({\mathcal {X}}\)  and \({\widetilde{z}_{x}}\in {\mathcal {Z}}\)  domains deviate from each other in appearance. Visually all three objective functions generate synthetic patches which are somewhat more blurry than actual ALS-based AGB predictions. Blurriness is a known weakness with generative models such as GANs [1]}, [2]} and is related to the up-sampling methods, i.e. transposed convolution, used in the second part of the \({G}\)  network. These up-sampling methods affects the models ability to correctly reproduce the spectral distribution in images or generate new images with sharp high frequent components such as edges [2]}. Although the qualitative comparison of the three models show that they perform similarly, the quantitative comparison in Tab. REF  clearly indicates that the WGAN-GP model is the worse among the three, having an RMSE which is almost twice as high as the RMSE for Vanilla GAN or LSGAN.
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